Evolutionary Compression of Deep Neural Networks for Biomedical Image Segmentation.

Abstract

Biomedical image segmentation is lately dominated by deep neural networks (DNNs) due to their surpassing expert-level performance. However, the existing DNN models for biomedical image segmentation are generally highly parameterized, which severely impede their deployment on real-time platforms and portable devices. To tackle this difficulty, we propose an evolutionary compression method (ECDNN) to automatically discover efficient DNN architectures for biomedical image segmentation. Different from the existing studies, ECDNN can optimize network loss and number of parameters simultaneously during the evolution, and search for a set of Pareto-optimal solutions in a single run, which is useful for quantifying the tradeoff in satisfying different objectives, and flexible for compressing DNN when preference information is uncertain. In particular, a set of novel genetic operators is proposed for automatically identifying less important filters over the whole network. Moreover, a pruning operator is designed for eliminating convolutional filters from layers involved in feature map concatenation, which is commonly adopted in DNN architectures for capturing multi-level features from biomedical images. Experiments carried out on compressing DNN for retinal vessel and neuronal membrane segmentation tasks show that ECDNN can not only improve the performance without any retraining but also discover efficient network architectures that well maintain the performance. The superiority of the proposed method is further validated by comparison with the state-of-the-art methods.